#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use 
# under the terms of the LICENSE.md file.
#
# For inquiries contact  george.drettakis@inria.fr
#

import torch
import numpy as np
from utils.general_utils import inverse_sigmoid, get_expon_lr_func, build_rotation
from torch import nn
import os
from random import randint
from utils.system_utils import mkdir_p
from plyfile import PlyData, PlyElement
from utils.sh_utils import RGB2SH
from simple_knn._C import distCUDA2
from utils.graphics_utils import projection_ndc
from utils.graphics_utils import BasicPointCloud, get_uniform_points_on_sphere_fibonacci, camera_project
from utils.general_utils import strip_symmetric, build_scaling_rotation
from arguments import ModelParams, OptimizationParams
from scene.embedding import MLP, PosEmbedding
from tqdm import tqdm


def save_ply(points, path):
    points = points.detach().clone().cpu().numpy()
    
    dtype = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'),
            ('nx', 'f4'), ('ny', 'f4'), ('nz', 'f4'),
            ('red', 'u1'), ('green', 'u1'), ('blue', 'u1')]
    
    normals = np.zeros_like(points)
    rgb = np.zeros_like(points)

    elements = np.empty(points.shape[0], dtype=dtype)
    attributes = np.concatenate((points, normals, rgb), axis=1)
    elements[:] = list(map(tuple, attributes))

    # Create the PlyData object and write to file
    vertex_element = PlyElement.describe(elements, 'vertex')
    ply_data = PlyData([vertex_element])
    ply_data.write(path)


class GaussianModel:

    def setup_functions(self):
        def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation):
            L = build_scaling_rotation(scaling_modifier * scaling, rotation)
            actual_covariance = L @ L.transpose(1, 2)
            symm = strip_symmetric(actual_covariance)
            return symm
        
        self.scaling_activation = torch.exp
        self.scaling_inverse_activation = torch.log
        self.covariance_activation = build_covariance_from_scaling_rotation
        self.opacity_activation = torch.sigmoid
        self.inverse_opacity_activation = inverse_sigmoid
        self.rotation_activation = torch.nn.functional.normalize


    def __init__(self, dataset: ModelParams):
        self.active_sh_degree = 0
        self.max_sh_degree = dataset.sh_degree  
        self._xyz = torch.empty(0)
        self._features_dc = torch.empty(0)
        self._features_rest = torch.empty(0)
        self._scaling = torch.empty(0)
        self._rotation = torch.empty(0)
        self._opacity = torch.empty(0)
        self.max_radii2D = torch.empty(0)
        self.xyz_gradient_accum = torch.empty(0)
        self.denom = torch.empty(0)
        self.optimizer = None
        self.percent_dense = 0
        self.spatial_lr_scale = 0
        self.setup_functions()
        
        self.sun_feat_dim = dataset.sun_feat_dim
        self.hid_dim = dataset.hid_dim
        self.n_theta_freqs = dataset.n_theta_freqs
        self.n_phi_freqs = dataset.n_phi_freqs
        self.n_density_freqs = dataset.n_density_freqs
        self.dis_times = dataset.dis_times
        self.num_sky_gaussians = dataset.num_sky_gaussians
        self.sample_ratio = dataset.sample_ratio
        
        self.theta_embedder = PosEmbedding(self.n_theta_freqs).cuda()
        self.phi_embedder = PosEmbedding(self.n_phi_freqs).cuda()
        self.density_embedder = PosEmbedding(self.n_density_freqs).cuda()
        
        sun_in_dim = self.sun_feat_dim + (2 * self.n_theta_freqs + 1) * 3 + 3
        # sun_in_dim = self.sun_feat_dim * 2
        self.sun_mlp = MLP(sun_in_dim, self.hid_dim, out_dim=1, n_layers=2, out_act=nn.Sigmoid()).cuda()
    
    def feature_encode(self, camera, theta: torch.Tensor, phi: torch.Tensor, density: torch.Tensor, visible_mask=None):
        if visible_mask is None:
            visible_mask = torch.full((self.get_xyz.shape[0]), 1).bool().cuda()   
        
        if theta.ndim != 2:
            theta = theta[None]
        if phi.ndim != 2:
            phi = phi[None]
        if density.ndim != 2:
            density = density[None]
        
        sun_feats = self.get_sun_features[visible_mask]
        embed_theta = self.theta_embedder(theta).repeat(sun_feats.shape[0], 1)
        embed_phi = self.phi_embedder(phi).repeat(sun_feats.shape[0], 1)
        embed_density = self.density_embedder(density).repeat(sun_feats.shape[0], 1)
        
        ob_view = self.get_xyz[visible_mask] - camera.camera_center
        ob_dist = ob_view.norm(dim=1, keepdim=True)     # (N, 1)
        ob_view = ob_view / ob_dist                     # (N, 3)
        
        # sample_sun_feat = self.get_sun_features[self.sample_idx][None].repeat(sun_feats.shape[0], 1)        
        # sun_colors = self.sun_mlp(torch.cat([sun_feats, sample_sun_feat], -1))
        
        xyz_emb = self.theta_embedder(self.get_xyz[self.sample_idx][None]).repeat(sun_feats.shape[0], 1) 
        sun_colors = self.sun_mlp(torch.cat([sun_feats, self.get_xyz[visible_mask], xyz_emb], -1))
        # sun_colors = self.sun_mlp(torch.cat([sun_feats, embed_phi, embed_theta, embed_density], -1))
        return sun_colors

    def capture(self):
        return (
            self.active_sh_degree,
            self._xyz,
            self._features_dc,
            self._features_rest,
            self._scaling,
            self._rotation,
            self._opacity,
            self.max_radii2D,
            self.xyz_gradient_accum,
            self.denom,
            self.optimizer.state_dict(),
            self.spatial_lr_scale,
        )
    
    def restore(self, model_args, training_args):
        (self.active_sh_degree, 
        self._xyz, 
        self._features_dc, 
        self._features_rest,
        self._scaling, 
        self._rotation, 
        self._opacity,
        self.max_radii2D, 
        xyz_gradient_accum, 
        denom,
        opt_dict, 
        self.spatial_lr_scale) = model_args
        self.training_setup(training_args)
        self.xyz_gradient_accum = xyz_gradient_accum
        self.denom = denom
        self.optimizer.load_state_dict(opt_dict)

    @property
    def get_sun_features(self):
        return self._sun_feat

    @property
    def get_scaling(self):
        return self.scaling_activation(self._scaling)
    
    @property
    def get_rotation(self):
        return self.rotation_activation(self._rotation)
    
    @property
    def get_xyz(self):
        return self._xyz
    
    @property
    def get_features(self):
        features_dc = self._features_dc
        features_rest = self._features_rest
        return torch.cat((features_dc, features_rest), dim=1)
    
    @property
    def get_opacity(self):
        return self.opacity_activation(self._opacity)
    
    def get_covariance(self, scaling_modifier = 1):
        return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation)

    def oneupSHdegree(self):
        if self.active_sh_degree < self.max_sh_degree:
            self.active_sh_degree += 1
            
    def sample_sun_pos(self, view):
        H, W = view.image_height, view.image_width
        uv, depths, in_mask = projection_ndc(self.points, view)
        in_bounds_mask = (uv[:, 0] < H) & (uv[:, 1] < W) & (uv[:, 0] >= 0) & (uv[:, 1] >= 0) 
        c_mask = torch.logical_and(in_bounds_mask, in_mask)        
        valid_indices = torch.nonzero(c_mask, as_tuple=False).squeeze(1)
        self.sample_idx = valid_indices[torch.randperm(valid_indices.shape[0])[0]]
        return self.points[self.sample_idx]

    def init_sky_points(self, points3D, cameras, num_points=100000):
        points = get_uniform_points_on_sphere_fibonacci(self.num_sky_gaussians, xnp=torch).float().cuda()
        mean = points3D.mean(0)[None]
        sky_distance = torch.quantile(torch.linalg.norm(points3D - mean, 2, -1), 0.97) * 100
        points = points * sky_distance
        points = points + mean             
        self.points = points
        self.sky_center = mean[0]
        self.sky_distance = sky_distance
        
    def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float, cameras):
        self.spatial_lr_scale = spatial_lr_scale
        points3D = torch.tensor(np.asarray(pcd.points)).float().cuda()
        self.init_sky_points(points3D, cameras, self.num_sky_gaussians)   
        print(f"Initializing sky gaussians: {self.points.shape[0]}")
        
        # save_ply(self.points, 'sun.ply')
        # breakpoint()

        dist2 = torch.clamp_min(distCUDA2(self.points), 0.0000001)
        scales = torch.log(torch.sqrt(dist2))[...,None].repeat(1, 3)
        rots = torch.zeros((self.points.shape[0], 4), device="cuda")
        rots[:, 0] = 1

        opacities = inverse_sigmoid(0.1 * torch.ones((self.points.shape[0], 1), dtype=torch.float, device="cuda"))
        sun_features = torch.randn((self.points.shape[0], self.sun_feat_dim)).float().cuda()

        self._xyz = nn.Parameter(self.points.requires_grad_(True))
        self._scaling = nn.Parameter(scales.requires_grad_(True))
        self._rotation = nn.Parameter(rots.requires_grad_(True))
        self._opacity = nn.Parameter(opacities.requires_grad_(True))
        self._sun_feat = nn.Parameter(sun_features.requires_grad_(True))
        self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")

    def training_setup(self, training_args: OptimizationParams):
        self.percent_dense = training_args.percent_dense
        self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
        self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")

        l = [
            # {'params': [self._xyz], 'lr': training_args.position_lr_init * self.spatial_lr_scale, "name": "xyz"},
            {'params': [self._opacity], 'lr': training_args.opacity_lr, "name": "opacity"},
            {'params': [self._scaling], 'lr': training_args.scaling_lr, "name": "scaling"},
            {'params': [self._rotation], 'lr': training_args.rotation_lr, "name": "rotation"},
            {'params': [self._sun_feat], 'lr': training_args.sun_feat_lr, "name": "sun_feat"},
            
            {'params': self.sun_mlp.parameters(), 'lr': training_args.sun_mlp_lr, "name": "sun_mlp"},
        ]
        
        self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15)
        self.xyz_scheduler_args = get_expon_lr_func(lr_init=training_args.position_lr_init*self.spatial_lr_scale,
                                                    lr_final=training_args.position_lr_final*self.spatial_lr_scale,
                                                    lr_delay_mult=training_args.position_lr_delay_mult,
                                                    max_steps=training_args.position_lr_max_steps)

    def update_learning_rate(self, iteration):
        ''' Learning rate scheduling per step '''
        for param_group in self.optimizer.param_groups:
            if param_group["name"] == "xyz":
                lr = self.xyz_scheduler_args(iteration)
                param_group['lr'] = lr
                return lr

    def construct_list_of_attributes(self):
        l = ['x', 'y', 'z', 'nx', 'ny', 'nz']
        for i in range(self._sun_feat.shape[1]):
            l.append('sun_feat_{}'.format(i))
        # for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]):
        #     l.append('f_dc_{}'.format(i))
        # for i in range(self._features_rest.shape[1]*self._features_rest.shape[2]):
        #     l.append('f_rest_{}'.format(i))
        l.append('opacity')
        for i in range(self._scaling.shape[1]):
            l.append('scale_{}'.format(i))
        for i in range(self._rotation.shape[1]):
            l.append('rot_{}'.format(i))
        return l

    def save_ply(self, path):
        mkdir_p(os.path.dirname(path))
        
        xyz = xyz = self._xyz
        sun_feat = self._sun_feat
        # f_dc = self._features_dc
        # f_rest = self._features_rest
        opacities = self._opacity
        scale = self._scaling
        rotation = self._rotation
        
        xyz = xyz.detach().cpu().numpy()
        normals = np.zeros_like(xyz)
        # f_dc = f_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
        # f_rest = f_rest.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
        opacities = opacities.detach().cpu().numpy()
        scale = scale.detach().cpu().numpy()
        rotation = rotation.detach().cpu().numpy()
        sun_feat = sun_feat.detach().cpu().numpy()

        dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()]

        elements = np.empty(xyz.shape[0], dtype=dtype_full)
        attributes = np.concatenate((xyz, normals, sun_feat, opacities, scale, rotation), axis=1)
        # attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1)
        elements[:] = list(map(tuple, attributes))
        el = PlyElement.describe(elements, 'vertex')
        PlyData([el]).write(path)

    def reset_opacity(self):
        opacities_new = inverse_sigmoid(torch.min(self.get_opacity, torch.ones_like(self.get_opacity)*0.01))
        optimizable_tensors = self.replace_tensor_to_optimizer(opacities_new, "opacity")
        self._opacity = optimizable_tensors["opacity"]

    def load_ply(self, path):
        plydata = PlyData.read(path)

        xyz = np.stack((np.asarray(plydata.elements[0]["x"]),
                        np.asarray(plydata.elements[0]["y"]),
                        np.asarray(plydata.elements[0]["z"])),  axis=1)
        opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis]

        # features_dc = np.zeros((xyz.shape[0], 3, 1))
        # features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"])
        # features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"])
        # features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"])

        # extra_f_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("f_rest_")]
        # extra_f_names = sorted(extra_f_names, key = lambda x: int(x.split('_')[-1]))
        # assert len(extra_f_names)==3*(self.max_sh_degree + 1) ** 2 - 3
        # features_extra = np.zeros((xyz.shape[0], len(extra_f_names)))
        # for idx, attr_name in enumerate(extra_f_names):
        #     features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name])
        # # Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC)
        # features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1))

        sun_feat_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("sun_feat_")]
        sun_feat_names = sorted(sun_feat_names, key = lambda x: int(x.split('_')[-1]))
        sun_feat = np.zeros((xyz.shape[0], len(sun_feat_names)))
        for idx, attr_name in enumerate(sun_feat_names):
            sun_feat[:, idx] = np.asarray(plydata.elements[0][attr_name])
        
        scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")]
        scale_names = sorted(scale_names, key = lambda x: int(x.split('_')[-1]))
        scales = np.zeros((xyz.shape[0], len(scale_names)))
        for idx, attr_name in enumerate(scale_names):
            scales[:, idx] = np.asarray(plydata.elements[0][attr_name])

        rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot")]
        rot_names = sorted(rot_names, key = lambda x: int(x.split('_')[-1]))
        rots = np.zeros((xyz.shape[0], len(rot_names)))
        for idx, attr_name in enumerate(rot_names):
            rots[:, idx] = np.asarray(plydata.elements[0][attr_name])
        
        self._xyz = nn.Parameter(torch.tensor(xyz, dtype=torch.float, device="cuda").requires_grad_(True))
        self._sun_feat = nn.Parameter(torch.tensor(sun_feat, dtype=torch.float, device="cuda").requires_grad_(True))
        # self._features_dc = nn.Parameter(torch.tensor(features_dc, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True))
        # self._features_rest = nn.Parameter(torch.tensor(features_extra, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True))
        self._opacity = nn.Parameter(torch.tensor(opacities, dtype=torch.float, device="cuda").requires_grad_(True))
        self._scaling = nn.Parameter(torch.tensor(scales, dtype=torch.float, device="cuda").requires_grad_(True))
        self._rotation = nn.Parameter(torch.tensor(rots, dtype=torch.float, device="cuda").requires_grad_(True))

        self.active_sh_degree = self.max_sh_degree

    def replace_tensor_to_optimizer(self, tensor, name):
        optimizable_tensors = {}
        for group in self.optimizer.param_groups:
            if group["name"] == name:
                stored_state = self.optimizer.state.get(group['params'][0], None)
                stored_state["exp_avg"] = torch.zeros_like(tensor)
                stored_state["exp_avg_sq"] = torch.zeros_like(tensor)

                del self.optimizer.state[group['params'][0]]
                group["params"][0] = nn.Parameter(tensor.requires_grad_(True))
                self.optimizer.state[group['params'][0]] = stored_state

                optimizable_tensors[group["name"]] = group["params"][0]
        return optimizable_tensors

    def _prune_optimizer(self, mask):
        optimizable_tensors = {}
        for group in self.optimizer.param_groups:
            stored_state = self.optimizer.state.get(group['params'][0], None)
            if stored_state is not None:
                stored_state["exp_avg"] = stored_state["exp_avg"][mask]
                stored_state["exp_avg_sq"] = stored_state["exp_avg_sq"][mask]

                del self.optimizer.state[group['params'][0]]
                group["params"][0] = nn.Parameter((group["params"][0][mask].requires_grad_(True)))
                self.optimizer.state[group['params'][0]] = stored_state

                optimizable_tensors[group["name"]] = group["params"][0]
            else:
                group["params"][0] = nn.Parameter(group["params"][0][mask].requires_grad_(True))
                optimizable_tensors[group["name"]] = group["params"][0]
        return optimizable_tensors

    def prune_points(self, mask):
        valid_points_mask = ~mask
        optimizable_tensors = self._prune_optimizer(valid_points_mask)

        self._xyz = optimizable_tensors["xyz"]
        self._features_dc = optimizable_tensors["f_dc"]
        self._features_rest = optimizable_tensors["f_rest"]
        self._opacity = optimizable_tensors["opacity"]
        self._scaling = optimizable_tensors["scaling"]
        self._rotation = optimizable_tensors["rotation"]

        self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask]

        self.denom = self.denom[valid_points_mask]
        self.max_radii2D = self.max_radii2D[valid_points_mask]

    def cat_tensors_to_optimizer(self, tensors_dict):
        optimizable_tensors = {}
        for group in self.optimizer.param_groups:
            assert len(group["params"]) == 1
            extension_tensor = tensors_dict[group["name"]]
            stored_state = self.optimizer.state.get(group['params'][0], None)
            if stored_state is not None:

                stored_state["exp_avg"] = torch.cat((stored_state["exp_avg"], torch.zeros_like(extension_tensor)), dim=0)
                stored_state["exp_avg_sq"] = torch.cat((stored_state["exp_avg_sq"], torch.zeros_like(extension_tensor)), dim=0)

                del self.optimizer.state[group['params'][0]]
                group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True))
                self.optimizer.state[group['params'][0]] = stored_state

                optimizable_tensors[group["name"]] = group["params"][0]
            else:
                group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True))
                optimizable_tensors[group["name"]] = group["params"][0]

        return optimizable_tensors

    def densification_postfix(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation):
        d = {"xyz": new_xyz,
        "f_dc": new_features_dc,
        "f_rest": new_features_rest,
        "opacity": new_opacities,
        "scaling" : new_scaling,
        "rotation" : new_rotation}

        optimizable_tensors = self.cat_tensors_to_optimizer(d)
        self._xyz = optimizable_tensors["xyz"]
        self._features_dc = optimizable_tensors["f_dc"]
        self._features_rest = optimizable_tensors["f_rest"]
        self._opacity = optimizable_tensors["opacity"]
        self._scaling = optimizable_tensors["scaling"]
        self._rotation = optimizable_tensors["rotation"]

        self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
        self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
        self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")

    def densify_and_split(self, grads, grad_threshold, scene_extent, N=2):
        n_init_points = self.get_xyz.shape[0]
        # Extract points that satisfy the gradient condition
        padded_grad = torch.zeros((n_init_points), device="cuda")
        padded_grad[:grads.shape[0]] = grads.squeeze()
        selected_pts_mask = torch.where(padded_grad >= grad_threshold, True, False)
        selected_pts_mask = torch.logical_and(selected_pts_mask,
                                              torch.max(self.get_scaling, dim=1).values > self.percent_dense*scene_extent)

        stds = self.get_scaling[selected_pts_mask].repeat(N,1)
        means =torch.zeros((stds.size(0), 3),device="cuda")
        samples = torch.normal(mean=means, std=stds)
        rots = build_rotation(self._rotation[selected_pts_mask]).repeat(N,1,1)
        new_xyz = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self.get_xyz[selected_pts_mask].repeat(N, 1)
        new_scaling = self.scaling_inverse_activation(self.get_scaling[selected_pts_mask].repeat(N,1) / (0.8*N))
        new_rotation = self._rotation[selected_pts_mask].repeat(N,1)
        new_features_dc = self._features_dc[selected_pts_mask].repeat(N,1,1)
        new_features_rest = self._features_rest[selected_pts_mask].repeat(N,1,1)
        new_opacity = self._opacity[selected_pts_mask].repeat(N,1)

        self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation)

        prune_filter = torch.cat((selected_pts_mask, torch.zeros(N * selected_pts_mask.sum(), device="cuda", dtype=bool)))
        self.prune_points(prune_filter)

    def densify_and_clone(self, grads, grad_threshold, scene_extent):
        # Extract points that satisfy the gradient condition
        selected_pts_mask = torch.where(torch.norm(grads, dim=-1) >= grad_threshold, True, False)
        selected_pts_mask = torch.logical_and(selected_pts_mask,
                                              torch.max(self.get_scaling, dim=1).values <= self.percent_dense*scene_extent)
        
        new_xyz = self._xyz[selected_pts_mask]
        new_features_dc = self._features_dc[selected_pts_mask]
        new_features_rest = self._features_rest[selected_pts_mask]
        new_opacities = self._opacity[selected_pts_mask]
        new_scaling = self._scaling[selected_pts_mask]
        new_rotation = self._rotation[selected_pts_mask]

        self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation)

    def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size):
        grads = self.xyz_gradient_accum / self.denom
        grads[grads.isnan()] = 0.0

        self.densify_and_clone(grads, max_grad, extent)
        self.densify_and_split(grads, max_grad, extent)

        prune_mask = (self.get_opacity < min_opacity).squeeze()
        if max_screen_size:
            big_points_vs = self.max_radii2D > max_screen_size
            big_points_ws = self.get_scaling.max(dim=1).values > 0.1 * extent
            prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws)
        self.prune_points(prune_mask)

        torch.cuda.empty_cache()

    def add_densification_stats(self, viewspace_point_tensor, update_filter):
        self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor.grad[update_filter,:2], dim=-1, keepdim=True)
        self.denom[update_filter] += 1